权重基因共表达网络分析在生物医学中的应用

高通量生物监测方法可以同时检测同一样本的上千个参数,其在生物医学中的应用越来越广泛,但如何系统地分析并从高通量数据中挖掘有用信息,仍是一项重要的课题。网络生物学的出现使人们对复杂生物系统有了更深刻的理解,组织/细胞功能执行具有模块化特点。目前,相关网络(Correlation network)被越来越多地应用于生物信息学,权重基因共表达网络分析(Weighted gene co-expression network analysis,WGCNA)是描述样品基因表达相关模式的一种系统生物学工具。在此,对WGCNA在疾病分型及预后、发病机制和其他相关领域研究进展作一个较为系统的综述。首先,对WGCNA的原理、分析流程和优势缺点进行总结。其次,介绍如何用WGCNA研究疾病、正常组织、药物、进化和基因组注释。最后,结合新高通量技术展望WGCNA应用新空间。以期科研工作者能够对WGCNA的应用有所了解。

Weighted gene co-expression network analysis in biomedicine research

High-throughput biological technologies are now widely applied in biology and medicine, allowing scientists to monitor thousands of parameters simultaneously in a specific sample. However, it is still an enormous challenge to mine useful information from high-throughput data. The emergence of network biology provides deeper insights into complex bio-system and reveals the modularity in tissue/cellular networks. Correlation networks are increasingly used in bioinformatics applications. Weighted gene co-expression network analysis (WGCNA) tool can detect clusters of highly correlated genes. Therefore, we systematically reviewed the application of WGCNA in the study of disease diagnosis, pathogenesis and other related fields. First, we introduced principle, workflow, advantages and disadvantages of WGCNA. Second, we presented the application of WGCNA in disease, physiology, drug, evolution and genome annotation. Then, we indicated the application of WGCNA in newly developed high-throughput methods. We hope this review will help to promote the application of WGCNA in biomedicine research.